TCC
Leonardo Fernandes Wink
15/09/2022
1 Anotações de coisas por fazer:
- Descobrir como colocar as estações no sentido correto montante -> jusante nos sumários
- not entirely necessary pq tenho os sumários mais aprimorados ainda no excel.
87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900
Aprender a segmentar o meu dataset por períodosaprender a criar uma nova coluna com a segmentação dos períodos- aprender a colocar a legenda dentro do gráfico
- reduzir o tamanho da legenda
corrigir os valores 0 de IQA pra NA- descobrir como conseguir a equação do lm
aprender a pivotar o sumário-> meu sumário do google docs ta batendo direitinho com o do R- descobrir se há outros TCCs com disponibilização de códigos
- correlação forte entre condutividade e Namon/Ptot/DBO
| 1990-2000 | 2000-2010 | 2010-2020 |
|---|---|---|
| 1990-2000 | 2000-2010 | 2010-2020 |
2 Instalar os pacotes
# install.packages(tidyverse)2.1 acessar os pacotes
# library(readr)
# library(rmarkdown)
# # library(qboxplot)
# library(readxl)
# library(pillar)
# library(dplyr)
# library(tidyverse)
# library(gapminder)
# library(knitr)
# library(kableExtra)
# library(ggpubr)
# library(gridExtra)
# library(modelsummary)
# library(gtsummary)
# library(GGally)
pacman::p_load(readr, rmarkdown, readxl,
pillar, dplyr, tidyverse,
gapminder, knitr, kableExtra,
gridExtra, #modelsummary,
gtsummary, ggplot2,
ggbeeswarm, GGally)
# pacman::p_load(tibbletime)knitr::knit_hooks$set(time_it = local({
now <- NULL
function(before, options) {
if (before) {
# record the current time before each chunk
now <<- Sys.time()
} else {
# calculate the time difference after a chunk
res <- difftime(Sys.time(), now)
# return a character string to show the time
paste("Time for this code chunk to run:", res)
}
}
}))
knitr::opts_chunk$set(time_it = TRUE)2.2 importando a planilha
3 data wrangling
plan_wide_19902020 <- plan_wide_19902020 %>%
mutate(IQA = ifelse(IQA == 0, NA, IQA))4 setting theme
theme_grafs <- function(bg = "white",
coloracao_letra = "black") {
theme(
plot.title = element_text(
hjust = 0.5,
color = coloracao_letra,
size = 19),
axis.title.x =
# element_text(
# color = coloracao_letra,
# size = 15,
# angle = 0,),
element_blank(),
axis.title.y = element_text(
color = coloracao_letra,
size = 15,
angle = 90),
axis.text.x = element_text(
color = coloracao_letra,
size = 17),
axis.text.y = element_text(
color = coloracao_letra,
size = 17,
angle = 0),
panel.background = element_rect(fill = bg),
plot.background = element_rect(fill = bg),
plot.margin = margin(l = 5, r = 10,
b = 5, t = 5)
)
}5 setting periodos
# p1 <- plan_wide_19902020 %>%
# filter(ANO_COLETA > "1990" &
# ANO_COLETA <= "2000")
#
# p2 <- plan_wide_19902020 %>%
# filter(ANO_COLETA > "2000" &
# ANO_COLETA <= "2010")
#
# p3 <- plan_wide_19902020 %>%
# filter(ANO_COLETA > "2010" &
# ANO_COLETA <= "2020")
# teste_all_periodos <- plan_wide_19902020 %>%
# filter(
# between(ANO_COLETA, 1990, 2000)
# )6 setting sumaries
p1 <- plan_wide_19902020 %>%
filter(ANO_COLETA > "1990" &
ANO_COLETA <= "2000")
p2 <- plan_wide_19902020 %>%
filter(ANO_COLETA > "2000" &
ANO_COLETA <= "2010")
p3 <- plan_wide_19902020 %>%
filter(ANO_COLETA > "2010" &
ANO_COLETA <= "2020")
# periodo = c(p1 <- plan_wide_19902020 %>%
# filter(ANO_COLETA > "1990" &
# ANO_COLETA <= "2000"),
#
# p2 <- plan_wide_19902020 %>%
# filter(ANO_COLETA > "2000" &
# ANO_COLETA <= "2010"),
#
# p3 <- plan_wide_19902020 %>%
# filter(ANO_COLETA > "2010" &
# ANO_COLETA <= "2020"))
# sumario <- function(parametros = parametros, periodo){
# plan_wide_19902020 %>%
# select(CODIGO, ., ANO_COLETA) %>%
# # filter(ANO_COLETA>"1990" &
# # ANO_COLETA<="2000") %>%
# group_by(CODIGO) %>%
# summarize(
# min =
# min(parametros,
# na.rm = TRUE),
# q1 =
# quantile(parametros, 0.25,
# na.rm = TRUE),
# median =
# median(parametros,
# na.rm = TRUE),
# mean =
# mean(parametros,
# na.rm= TRUE),
# q3 =
# quantile(parametros, 0.75,
# na.rm = TRUE),
# max =
# max(parametros,
# na.rm = TRUE))
# }
# plan_wide_19902020 %>%
# sumario(parametros = DBO)
# sum_IQA_p1 <- plan_wide_19902020 %>%
# select(CODIGO, IQA, ANO_COLETA) %>%
# filter(ANO_COLETA>"1990" &
# ANO_COLETA<="2000") %>%
# group_by(CODIGO) %>%
# summarize(
# min =
# min(IQA,
# na.rm = TRUE),
# q1 =
# quantile(IQA, 0.25,
# na.rm = TRUE),
# median =
# median(IQA,
# na.rm = TRUE),
# mean =
# mean(IQA,
# na.rm= TRUE),
# q3 =
# quantile(IQA, 0.75,
# na.rm = TRUE),
# max =
# max(IQA,
# na.rm = TRUE))7 Parâmetros físico-químicos
7.0.1 Oxigênio Dissolvido
# par_od <- plan_wide_19902020 %>%
# select(CODIGO, `Oxigênio dissolvido`) %>%
# group_nest(CODIGO)
# parametros_IQA
# parametros <- colnames(parametros_IQA)[]
plan_wide_19902020 %>%
ggplot(
aes(CODIGO, `Oxigênio dissolvido`)
)+
annotate("rect",
xmin = -Inf, xmax = Inf,
ymin = -Inf, ymax = 2,
alpha = 1,
fill = "#ac5079")+ # >pior classe
annotate("rect",
xmin = -Inf, xmax = Inf,
ymin = 2, ymax = 4,
alpha = 1,
fill = "#eb5661")+ #classe 4
annotate("rect",
xmin = -Inf, xmax = Inf,
ymin = 4, ymax = 5,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=5,
ymax=6,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=6,
ymax=Inf,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(
geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65)
)+
geom_boxplot(
fill = '#F8F8FF',
color = "black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7
)+
labs(
title = "Oxigênio Dissolvido no período 1990-2000",
x = "Estação",
y = "mg/L"
)+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(
expand = expansion(mult = c(0,0)),
n.breaks = 11,
limits = c(-1,21)
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)## Warning: Removed 92 rows containing non-finite values (stat_boxplot).
## Removed 92 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 92 rows containing missing values (position_quasirandom).
Oxigênio Dissolvido no período 1990-2000
grid.arrange(od_p1, od_p2, od_p3, ncol = 3)
Oxigênio Dissolvido no período 1990-2020
ggsave("od_p1.png",
plot = od_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 7 rows containing non-finite values (stat_boxplot).
## Removed 7 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing missing values (position_quasirandom).ggsave("od_p2.png",
plot = od_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
## Removed 54 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 54 rows containing missing values (position_quasirandom).ggsave("od_p3.png",
plot = od_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 31 rows containing non-finite values (stat_boxplot).
## Removed 31 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing missing values (position_quasirandom).ggsave("od_3periodos_2.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(od_p1, od_p2, od_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Removed 7 rows containing non-finite values (stat_boxplot).## Warning: Removed 7 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing missing values (position_quasirandom).## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
## Removed 54 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 54 rows containing missing values (position_quasirandom).## Warning: Removed 31 rows containing non-finite values (stat_boxplot).
## Removed 31 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
grid.arrange(iqaod_p1, iqaod_p2, iqaod_p3, ncol = 3)
Resultado que quero chegar pro sumário
| variable | Estação 1 | Estação 2 | Estação 3 | Estação 4 |
|---|---|---|---|---|
| max | 15 | 17 | 16 | 14 |
| med | 14 | 16 | 15 | 13 |
| min | 13 | 15 | 14 | 12 |
| n | 15 | 12 | 3 | 4 |
## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.8 4.9 6.4 5.99 7.3 10.8
## 2 87398900 2 5.6 6.9 6.78 8 10.5
## 3 87398950 2.5 4.4 5.95 5.98 7.1 10.3
## 4 87398980 4.2 6 6.3 7.01 8.2 12.1
## 5 87405500 0.1 1.9 4.2 4.22 6 19.9
## 6 87406900 0.1 0.25 2.6 2.98 5 10.2
## 7 87409900 0.1 1.4 2.9 3.60 5.65 11.1## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.4 3.5 4.9 5.01 6.65 10.9
## 2 87398900 1.9 4 5.5 5.33 6.6 12
## 3 87398950 1.7 3.2 5.3 5.06 6.18 8.9
## 4 87398980 1.2 3.8 5.6 5.38 6.6 9.2
## 5 87405500 0.2 1.4 2.55 3.28 4 14.2
## 6 87406900 0 1.1 1.9 2.59 3.15 16
## 7 87409900 0 0.7 2.3 3.12 3.7 10.6## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.38 3.11 4.41 4.57 6.2 12.4
## 2 87398900 3.52 5.25 5.96 6.61 7.3 13.8
## 3 87398950 1.62 3.68 4.92 5.28 6.64 11.9
## 4 87398980 3.37 5.5 6.17 6.48 7.14 13.1
## 5 87405500 0.2 1.3 2.53 2.83 3.66 9.8
## 6 87406900 0.1 0.865 2.4 2.43 3.05 9.1
## 7 87409900 0.1 0.92 2.03 2.43 3.5 8.17.0.2 Demanda Bioquímica de Oxigênio
grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3)
(sum_dbo_p1 <- plan_wide_19902020 %>%
select(CODIGO, DBO, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(DBO,
na.rm = TRUE),
q1 =
quantile(DBO, 0.25,
na.rm = TRUE),
median =
median(DBO,
na.rm = TRUE),
mean =
mean(DBO,
na.rm= TRUE),
q3 =
quantile(DBO, 0.75,
na.rm = TRUE),
max =
max(DBO,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 2 1.86 2 13
## 2 87398900 1 1 1 1.52 2 6
## 3 87398950 1 1 1 1.66 2 6
## 4 87398980 1 1 1 1.13 1 2
## 5 87405500 1 2 3 5.37 5 64
## 6 87406900 1 4 5 9 11 26
## 7 87409900 2 3 4 6.97 9.5 31(sum_dbo_p2 <- plan_wide_19902020 %>%
select(CODIGO, DBO, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(DBO,
na.rm = TRUE),
q1 =
quantile(DBO, 0.25,
na.rm = TRUE),
median =
median(DBO,
na.rm = TRUE),
mean =
mean(DBO,
na.rm= TRUE),
q3 =
quantile(DBO, 0.75,
na.rm = TRUE),
max =
max(DBO,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 1 1.58 2 5
## 2 87398900 1 1 1 1.40 2 5
## 3 87398950 1 1 1 1.66 2 5
## 4 87398980 1 1 1 1.30 1 5
## 5 87405500 1 2 4 4.67 6.5 14
## 6 87406900 1 3 5 6.53 8 28
## 7 87409900 1 3 6 6.31 9 15(sum_dbo_p3 <- plan_wide_19902020 %>%
select(CODIGO, DBO, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(DBO,
na.rm = TRUE),
q1 =
quantile(DBO, 0.25,
na.rm = TRUE),
median =
median(DBO,
na.rm = TRUE),
mean =
mean(DBO,
na.rm= TRUE),
q3 =
quantile(DBO, 0.75,
na.rm = TRUE),
max =
max(DBO,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 1.5 2.15 3 7
## 2 87398900 1 1 1 1.51 2 5
## 3 87398950 1 1 2 2.65 2 18
## 4 87398980 1 1 1 1.32 2 2
## 5 87405500 1 3 4 5.28 6.25 21
## 6 87406900 1 3 5 6.58 10 24
## 7 87409900 1 3 4.5 6.18 8 18ggsave("dbo_p1.png",
plot = dbo_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 22 rows containing non-finite values (stat_boxplot).
## Removed 22 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing missing values (position_quasirandom).ggsave("dbo_p2.png",
plot = dbo_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 30 rows containing non-finite values (stat_boxplot).
## Removed 30 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing missing values (position_quasirandom).ggsave("dbo_p3.png",
plot = dbo_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Removed 8 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing missing values (position_quasirandom).ggsave("dbo_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 22 rows containing non-finite values (stat_boxplot).
## Removed 22 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 30 rows containing non-finite values (stat_boxplot).
## Removed 30 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Removed 8 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments ###
Fósforo total
(ptot_p1<-ggplot(plan_wide_19902020%>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
`Fósforo total`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 1990-2000",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
trans = "log10")+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(ptot_p2 <- ggplot(plan_wide_19902020%>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
`Fósforo total`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 2000-2010",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
trans = "log10")+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(ptot_p3 <- ggplot(plan_wide_19902020%>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
`Fósforo total`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 2010-2020",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
trans = "log10")+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3)
(sum_ptot_p1 <- plan_wide_19902020 %>%
select(CODIGO, `Fósforo total`, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Fósforo total`, na.rm = TRUE),
q1 =
quantile(`Fósforo total`, 0.25, na.rm = TRUE),
median =
median(`Fósforo total`, na.rm = TRUE),
mean =
mean(`Fósforo total`, na.rm= TRUE),
q3 =
quantile(`Fósforo total`, 0.75, na.rm = TRUE),
max =
max(`Fósforo total`, na.rm = TRUE)))## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.0097 0.0593 0.0881 0.123 0.14 0.863
## 2 87398900 0.0023 0.0468 0.0678 0.0747 0.0883 0.247
## 3 87398950 0.0202 0.0544 0.0737 0.0751 0.0904 0.179
## 4 87398980 0.01 0.0254 0.0547 0.0708 0.114 0.189
## 5 87405500 0.017 0.171 0.281 0.417 0.492 2.32
## 6 87406900 0.156 0.270 0.508 0.785 1.07 2.79
## 7 87409900 0.107 0.258 0.384 0.489 0.712 1.53(sum_ptot_p2 <- plan_wide_19902020 %>%
select(CODIGO, `Fósforo total`, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Fósforo total`, na.rm = TRUE),
q1 =
quantile(`Fósforo total`, 0.25, na.rm = TRUE),
median =
median(`Fósforo total`, na.rm = TRUE),
mean =
mean(`Fósforo total`, na.rm= TRUE),
q3 =
quantile(`Fósforo total`, 0.75, na.rm = TRUE),
max =
max(`Fósforo total`, na.rm = TRUE)))## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.025 0.094 0.131 0.148 0.16 0.637
## 2 87398900 0.015 0.0764 0.104 0.140 0.164 0.646
## 3 87398950 0.036 0.116 0.171 0.180 0.207 0.485
## 4 87398980 0.0115 0.052 0.076 0.101 0.103 1
## 5 87405500 0.046 0.261 0.406 0.547 0.681 1.98
## 6 87406900 0.056 0.338 0.599 0.752 0.967 3.49
## 7 87409900 0.043 0.325 0.624 0.677 0.989 1.57(sum_ptot_p3 <- plan_wide_19902020 %>%
select(CODIGO, `Fósforo total`, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Fósforo total`, na.rm = TRUE),
q1 =
quantile(`Fósforo total`, 0.25, na.rm = TRUE),
median =
median(`Fósforo total`, na.rm = TRUE),
mean =
mean(`Fósforo total`, na.rm= TRUE),
q3 =
quantile(`Fósforo total`, 0.75, na.rm = TRUE),
max =
max(`Fósforo total`, na.rm = TRUE)))## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.061 0.118 0.163 0.166 0.186 0.381
## 2 87398900 0.057 0.0935 0.130 0.163 0.168 0.444
## 3 87398950 0.07 0.132 0.156 0.292 0.221 3.11
## 4 87398980 0.019 0.0625 0.106 0.144 0.170 0.59
## 5 87405500 0.013 0.187 0.332 0.361 0.45 0.803
## 6 87406900 0.089 0.254 0.364 0.448 0.560 1.26
## 7 87409900 0.203 0.259 0.369 0.488 0.564 1.7ggsave("ptot_p1.png",
plot = ptot_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 47 rows containing non-finite values (stat_boxplot).
## Removed 47 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 47 rows containing missing values (position_quasirandom).ggsave("ptot_p2.png",
plot = ptot_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 31 rows containing non-finite values (stat_boxplot).
## Removed 31 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing missing values (position_quasirandom).ggsave("ptot_p3.png",
plot = ptot_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
## Removed 54 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 54 rows containing missing values (position_quasirandom).ggsave("ptot_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 47 rows containing non-finite values (stat_boxplot).
## Removed 47 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 47 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 31 rows containing non-finite values (stat_boxplot).
## Removed 31 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
## Removed 54 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 54 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
7.0.3 Escherichia coli
(ecoli_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
`Escherichia coli`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 1990-2000",
x="Estação",
y="NMP/100mL")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(ecoli_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
`Escherichia coli`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 2000-2010",
x="Estação",
y="NMP/100mL")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(ecoli_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
`Escherichia coli`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 2010-2020",
x="Estação",
y="NMP/100mL")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3)
(sum_ecoli_p1 <- plan_wide_19902020 %>%
select(CODIGO, `Escherichia coli`, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Escherichia coli`,
na.rm = TRUE),
q1 =
quantile(`Escherichia coli`, 0.25,
na.rm = TRUE),
median =
median(`Escherichia coli`,
na.rm = TRUE),
mean =
mean(`Escherichia coli`,
na.rm= TRUE),
q3 =
quantile(`Escherichia coli`, 0.75,
na.rm = TRUE),
max =
max(`Escherichia coli`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 32 136 240 854. 720 19200
## 2 87398900 16 68 160 548. 480 7760
## 3 87398950 2.4 12.8 268 4039. 10000 28000
## 4 87398980 4 160 243. 2907. 446 25600
## 5 87405500 1.6 12.8 24 545. 128 18400
## 6 87406900 13.6 61.6 192 718. 414 12800
## 7 87409900 2.4 12.8 64 97.7 128 720(sum_ecoli_p2 <- plan_wide_19902020 %>%
select(CODIGO, `Escherichia coli`, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Escherichia coli`,
na.rm = TRUE),
q1 =
quantile(`Escherichia coli`, 0.25,
na.rm = TRUE),
median =
median(`Escherichia coli`,
na.rm = TRUE),
mean =
mean(`Escherichia coli`,
na.rm= TRUE),
q3 =
quantile(`Escherichia coli`, 0.75,
na.rm = TRUE),
max =
max(`Escherichia coli`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 21.6 91 150 1335. 308 27200
## 2 87398900 11 70 133. 444. 414. 2600
## 3 87398950 20 400 720 935. 1120 5500
## 4 87398980 24 110. 195 410. 289. 8800
## 5 87405500 4.7 162 2400 25445. 12950 490000
## 6 87406900 8 172 12800 66370. 62300 650000
## 7 87409900 16 7355. 35500 72440. 68750 460000(sum_ecoli_p3 <- plan_wide_19902020 %>%
select(CODIGO, `Escherichia coli`, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Escherichia coli`,
na.rm = TRUE),
q1 =
quantile(`Escherichia coli`, 0.25,
na.rm = TRUE),
median =
median(`Escherichia coli`,
na.rm = TRUE),
mean =
mean(`Escherichia coli`,
na.rm= TRUE),
q3 =
quantile(`Escherichia coli`, 0.75,
na.rm = TRUE),
max =
max(`Escherichia coli`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 90 155. 260 409. 451 2420
## 2 87398900 10 52.8 107 245. 313 1553.
## 3 87398950 108. 250 487 1424. 1553. 10462
## 4 87398980 40.8 140. 242. 529. 738. 2400
## 5 87405500 632 8965 19232. 109992. 70750 1400000
## 6 87406900 1440 23100 34500 230828. 140500 3400000
## 7 87409900 2000 20100 38400 83128. 83680 345000ggsave("ecoli_p1.png",
plot = ecoli_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Removed 15 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing missing values (position_quasirandom).ggsave("ecoli_p2.png",
plot = ecoli_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 66 rows containing non-finite values (stat_boxplot).
## Removed 66 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 66 rows containing missing values (position_quasirandom).ggsave("ecoli_p3.png",
plot = ecoli_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 14 rows containing missing values (position_quasirandom).ggsave("ecoli_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Removed 15 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 66 rows containing non-finite values (stat_boxplot).
## Removed 66 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 66 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 14 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments ###
Nitrogênio amoniacal
(namon_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
`Nitrogênio total`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=13.3,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3.7,
ymax=13.3,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3.7,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Nitrogênio amoniacal no período 1990-2000",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = .001,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(namon_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
`Nitrogênio total`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=13.3,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3.7,
ymax=13.3,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3.7,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Nitrogênio amoniacal no período 2000-2010",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = .001,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(namon_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
`Nitrogênio total`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=13.3,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3.7,
ymax=13.3,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3.7,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Nitrogênio amoniacal no período 2010-2020",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = .001,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)
(sum_namon_p1 <- plan_wide_19902020 %>%
select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Nitrogênio total`,
na.rm = TRUE),
q1 =
quantile(`Nitrogênio total`, 0.25,
na.rm = TRUE),
median =
median(`Nitrogênio total`,
na.rm = TRUE),
mean =
mean(`Nitrogênio total`,
na.rm= TRUE),
q3 =
quantile(`Nitrogênio total`, 0.75,
na.rm = TRUE),
max =
max(`Nitrogênio total`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.44 0.842 1.00 1.22 1.34 3.81
## 2 87398900 0.22 0.82 1 1.09 1.25 4.86
## 3 87398950 0.51 0.83 1.02 1.06 1.19 2.16
## 4 87398980 0.549 0.68 0.755 0.872 1.01 1.85
## 5 87405500 0.51 1.53 2.94 5.27 6.77 21.6
## 6 87406900 1.34 2.60 4.56 7.58 11.2 29.1
## 7 87409900 0.5 1.98 4.29 5.18 7.01 19.6(sum_namon_p2 <- plan_wide_19902020 %>%
select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Nitrogênio total`,
na.rm = TRUE),
q1 =
quantile(`Nitrogênio total`, 0.25,
na.rm = TRUE),
median =
median(`Nitrogênio total`,
na.rm = TRUE),
mean =
mean(`Nitrogênio total`,
na.rm= TRUE),
q3 =
quantile(`Nitrogênio total`, 0.75,
na.rm = TRUE),
max =
max(`Nitrogênio total`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.18 0.885 0.992 1.80 1.46 23.2
## 2 87398900 0.48 0.894 1.13 1.38 1.57 7.92
## 3 87398950 0.57 1.26 1.45 1.43 1.71 1.98
## 4 87398980 0.19 0.685 0.79 1.05 1.10 5.2
## 5 87405500 0.968 2 3.29 5.45 6.60 21.7
## 6 87406900 0.77 2.4 4.54 7.30 10.2 39.1
## 7 87409900 1.62 2.5 6.97 7.92 10.6 21.5(sum_namon_p3 <- plan_wide_19902020 %>%
select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Nitrogênio total`,
na.rm = TRUE),
q1 =
quantile(`Nitrogênio total`, 0.25,
na.rm = TRUE),
median =
median(`Nitrogênio total`,
na.rm = TRUE),
mean =
mean(`Nitrogênio total`,
na.rm= TRUE),
q3 =
quantile(`Nitrogênio total`, 0.75,
na.rm = TRUE),
max =
max(`Nitrogênio total`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.222 0.89 1.11 1.24 1.41 2.56
## 2 87398900 0.095 0.883 1.02 1.29 1.40 4.25
## 3 87398950 0.612 1.04 1.43 1.90 2.06 9.5
## 4 87398980 0.216 0.973 1.12 1.22 1.58 2.32
## 5 87405500 1.12 2.03 3.14 4.50 5.93 22.0
## 6 87406900 1.37 2.40 5.58 6.47 7.58 25
## 7 87409900 1.11 3 6.15 7.29 7.75 36ggsave("namon_p1.png",
plot = namon_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 102 rows containing non-finite values (stat_boxplot).
## Removed 102 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 102 rows containing missing values (position_quasirandom).ggsave("namon_p2.png",
plot = namon_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 110 rows containing non-finite values (stat_boxplot).
## Removed 110 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 110 rows containing missing values (position_quasirandom).ggsave("namon_p3.png",
plot = namon_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 70 rows containing non-finite values (stat_boxplot).
## Removed 70 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 70 rows containing missing values (position_quasirandom).ggsave("namon_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 102 rows containing non-finite values (stat_boxplot).
## Removed 102 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 102 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 110 rows containing non-finite values (stat_boxplot).
## Removed 110 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 110 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 70 rows containing non-finite values (stat_boxplot).
## Removed 70 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 70 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
7.0.4 Turbidez
(turb_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
Turbidez))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=100,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=40,
ymax=100,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=40,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Turbidez no período 1990-2000",
x="Estação",
y="UNT")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(turb_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
Turbidez))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=100,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=40,
ymax=100,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=40,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Turbidez no período 2000-2010",
x="Estação",
y="UNT")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(turb_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
Turbidez))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=100,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=40,
ymax=100,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=40,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Turbidez no período 2010-2020",
x="Estação",
y="UNT")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)
(sum_turb_p1 <- plan_wide_19902020 %>%
select(CODIGO, Turbidez, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Turbidez,
na.rm = TRUE),
q1 =
quantile(Turbidez, 0.25,
na.rm = TRUE),
median =
median(Turbidez,
na.rm = TRUE),
mean =
mean(Turbidez,
na.rm= TRUE),
q3 =
quantile(Turbidez, 0.75,
na.rm = TRUE),
max =
max(Turbidez,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 6.2 19 34.5 63.5 67 461
## 2 87398900 9 19 49.5 61.5 73.8 460
## 3 87398950 9.6 16 22 33.3 48.8 144
## 4 87398980 16 32.8 43 66.8 90.5 190
## 5 87405500 8.5 23.5 47 47.5 58 159
## 6 87406900 33 54.8 67 77.7 81.5 199
## 7 87409900 5.8 15 25 32.2 48 76(sum_turb_p2 <- plan_wide_19902020 %>%
select(CODIGO, Turbidez, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Turbidez,
na.rm = TRUE),
q1 =
quantile(Turbidez, 0.25,
na.rm = TRUE),
median =
median(Turbidez,
na.rm = TRUE),
mean =
mean(Turbidez,
na.rm= TRUE),
q3 =
quantile(Turbidez, 0.75,
na.rm = TRUE),
max =
max(Turbidez,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 9 41.2 55.5 71.1 74.2 428
## 2 87398900 39 57 78 107. 116. 475
## 3 87398950 39 47 64 96.5 90 330
## 4 87398980 24 37 50 64.5 87 176
## 5 87405500 32 46 63.5 70.3 76 341
## 6 87406900 35 49 62 69.9 75.5 284
## 7 87409900 40 45 60 70.4 90 151(sum_turb_p3 <- plan_wide_19902020 %>%
select(CODIGO, Turbidez, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Turbidez,
na.rm = TRUE),
q1 =
quantile(Turbidez, 0.25,
na.rm = TRUE),
median =
median(Turbidez,
na.rm = TRUE),
mean =
mean(Turbidez,
na.rm= TRUE),
q3 =
quantile(Turbidez, 0.75,
na.rm = TRUE),
max =
max(Turbidez,
na.rm = TRUE))
) ## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 8.52 16.4 29 33.3 43 85
## 2 87398900 14.8 39.2 48.3 66.7 73.4 299
## 3 87398950 16 29.9 41 51.6 65 230
## 4 87398980 11 19.4 33.6 39.5 42.2 110.
## 5 87405500 10.0 29.0 41 42.9 54.5 131
## 6 87406900 9.62 23 39 41.2 52 122
## 7 87409900 9.68 22.0 34.0 40.5 47 182.ggsave("turb_p1.png",
plot = turb_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 56 rows containing non-finite values (stat_boxplot).
## Removed 56 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 56 rows containing missing values (position_quasirandom).ggsave("turb_p2.png",
plot = turb_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 74 rows containing non-finite values (stat_boxplot).
## Removed 74 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing missing values (position_quasirandom).ggsave("turb_p3.png",
plot = turb_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 14 rows containing missing values (position_quasirandom).ggsave("turb_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 56 rows containing non-finite values (stat_boxplot).
## Removed 56 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 56 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 74 rows containing non-finite values (stat_boxplot).
## Removed 74 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing missing values (position_quasirandom).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 14 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
7.0.5 pH
(pH_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
pH))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=6,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=9,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=6,
ymax=9,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "pH no período 1990-2000",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 8,
limits = c(4,11),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(pH_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
pH))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=6,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=9,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=6,
ymax=9,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "pH no período 2000-2010",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 8,
limits = c(4,11),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(pH_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
pH))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=6,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=9,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=6,
ymax=9,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "pH no período 2010-2020",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 8,
limits = c(4,11),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)
(sum_pH_p1 <- plan_wide_19902020 %>%
select(CODIGO, pH, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5 6.18 6.59 6.51 6.82 7.9
## 2 87398900 5.2 6 6.3 6.33 6.63 7.9
## 3 87398950 5.4 6.29 6.4 6.49 6.72 8.1
## 4 87398980 5.3 5.93 6.2 6.16 6.3 7.3
## 5 87405500 5 6.3 6.4 6.47 6.7 9.3
## 6 87406900 5.5 6.18 6.45 6.43 6.8 7.3
## 7 87409900 4.5 6.2 6.4 6.44 6.7 7.4(sum_pH_p2 <- plan_wide_19902020 %>%
select(CODIGO, pH, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
) ## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5.3 6.3 6.6 6.59 6.88 7.9
## 2 87398900 5.5 6.4 6.65 6.63 6.9 7.5
## 3 87398950 6 6.6 6.8 6.89 7.25 7.6
## 4 87398980 5.8 6.3 6.5 6.63 7 7.5
## 5 87405500 5.2 6.4 6.6 6.68 6.9 8.3
## 6 87406900 5.5 6.4 6.7 6.66 6.9 8.6
## 7 87409900 5.8 6.5 6.8 6.77 7 8.4(sum_pH_p3 <- plan_wide_19902020 %>%
select(CODIGO, pH, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5.47 6.28 6.42 6.47 6.60 7.3
## 2 87398900 5.68 6.36 6.5 6.57 6.84 7.4
## 3 87398950 5.71 6.28 6.46 6.46 6.68 7
## 4 87398980 5.42 6.10 6.36 6.39 6.6 7.2
## 5 87405500 5.64 6.34 6.5 6.49 6.7 7.01
## 6 87406900 5.6 6.4 6.48 6.51 6.77 7.3
## 7 87409900 5.59 6.46 6.6 6.57 6.76 7.2ggsave("pH_p1.png",
plot = pH_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
## Removed 1 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing missing values (position_quasirandom).ggsave("pH_p2.png",
plot = pH_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 73 rows containing non-finite values (stat_boxplot).
## Removed 73 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 73 rows containing missing values (position_quasirandom).ggsave("pH_p3.png",
plot = pH_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 14 rows containing missing values (position_quasirandom).ggsave("pH_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).## Warning: Removed 1 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing missing values (position_quasirandom).## Warning: Removed 73 rows containing non-finite values (stat_boxplot).
## Removed 73 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 73 rows containing missing values (position_quasirandom).## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 14 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
7.0.6 Sólidos totais
(SolTot_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
`Sólidos totais`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Sólidos totais no período 1990-2000",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(SolTot_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
`Sólidos totais`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Sólidos totais no período 2000-2010",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
(SolTot_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
`Sólidos totais`))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Sólidos totais no período 2010-2020",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)
(sum_SolTot_p1 <- plan_wide_19902020 %>%
select(CODIGO, `Sólidos totais`, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Sólidos totais`,
na.rm = TRUE),
q1 =
quantile(`Sólidos totais`, 0.25,
na.rm = TRUE),
median =
median(`Sólidos totais`,
na.rm = TRUE),
mean =
mean(`Sólidos totais`,
na.rm= TRUE),
q3 =
quantile(`Sólidos totais`, 0.75,
na.rm = TRUE),
max =
max(`Sólidos totais`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 46 84.5 95 122. 120 510
## 2 87398900 18 74.5 97 111. 122. 474
## 3 87398950 10 76.5 91 90.9 106. 155
## 4 87398980 48 63.5 81.5 104. 126. 337
## 5 87405500 70 101 121 133. 151 361
## 6 87406900 89 118 155 165. 210 279
## 7 87409900 20 99.5 122 128. 143 381(sum_SolTot_p2 <- plan_wide_19902020 %>%
select(CODIGO, `Sólidos totais`, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Sólidos totais`,
na.rm = TRUE),
q1 =
quantile(`Sólidos totais`, 0.25,
na.rm = TRUE),
median =
median(`Sólidos totais`,
na.rm = TRUE),
mean =
mean(`Sólidos totais`,
na.rm= TRUE),
q3 =
quantile(`Sólidos totais`, 0.75,
na.rm = TRUE),
max =
max(`Sólidos totais`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 28 80 100 111. 123. 412
## 2 87398900 42 82 102. 128. 140. 489
## 3 87398950 46 94.2 108. 126. 127. 318
## 4 87398980 40 61 77 85.3 96 228
## 5 87405500 48 102 133 148. 170. 522
## 6 87406900 50 109 134. 154. 170. 670
## 7 87409900 56 112. 156 167. 190. 599(sum_SolTot_p3 <- plan_wide_19902020 %>%
select(CODIGO, `Sólidos totais`, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(`Sólidos totais`,
na.rm = TRUE),
q1 =
quantile(`Sólidos totais`, 0.25,
na.rm = TRUE),
median =
median(`Sólidos totais`,
na.rm = TRUE),
mean =
mean(`Sólidos totais`,
na.rm= TRUE),
q3 =
quantile(`Sólidos totais`, 0.75,
na.rm = TRUE),
max =
max(`Sólidos totais`,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 61 69 90 82.8 96 101
## 2 87398900 41 77 104 120. 127 308
## 3 87398950 45 93 101 109. 117 221
## 4 87398980 55 62.8 80 79.9 95 109
## 5 87405500 83 89.2 108. 124. 162. 195
## 6 87406900 50 106 117 135. 163 246
## 7 87409900 75 103 115 131. 145 251ggsave("SolTot_p1.png",
plot = SolTot_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 10 rows containing non-finite values (stat_boxplot).
## Removed 10 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 10 rows containing missing values (position_quasirandom).ggsave("SolTot_p2.png",
plot = SolTot_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 7 rows containing non-finite values (stat_boxplot).
## Removed 7 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing missing values (position_quasirandom).ggsave("SolTot_p3.png",
plot = SolTot_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 125 rows containing non-finite values (stat_boxplot).
## Removed 125 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 125 rows containing missing values (position_quasirandom).ggsave("SolTot_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Removed 10 rows containing non-finite values (stat_boxplot).## Warning: Removed 10 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 10 rows containing missing values (position_quasirandom).## Warning: Removed 7 rows containing non-finite values (stat_boxplot).
## Removed 7 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing missing values (position_quasirandom).## Warning: Removed 125 rows containing non-finite values (stat_boxplot).
## Removed 125 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 125 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
7.0.7 IQA
grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)
(sum_IQA_p1 <- plan_wide_19902020 %>%
select(CODIGO, IQA, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(IQA,
na.rm = TRUE),
q1 =
quantile(IQA, 0.25,
na.rm = TRUE),
median =
median(IQA,
na.rm = TRUE),
mean =
mean(IQA,
na.rm= TRUE),
q3 =
quantile(IQA, 0.75,
na.rm = TRUE),
max =
max(IQA,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 27.0 35.7 40.9 40.7 46.2 52.2
## 2 87398900 27.8 37.9 42.9 43.0 48.0 58.5
## 3 87398950 32.8 36.8 41.4 43.2 48.6 61.9
## 4 87398980 29.2 35.8 40.4 40.3 44.8 51.9
## 5 87405500 24.8 34.9 41.2 40.3 46.9 57.6
## 6 87406900 24.7 31.3 37.8 37.4 44.4 49.0
## 7 87409900 23.6 31.9 37.1 38.8 46.2 55.4(sum_IQA_p2 <- plan_wide_19902020 %>%
select(CODIGO, IQA, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(IQA,
na.rm = TRUE),
q1 =
quantile(IQA, 0.25,
na.rm = TRUE),
median =
median(IQA,
na.rm = TRUE),
mean =
mean(IQA,
na.rm= TRUE),
q3 =
quantile(IQA, 0.75,
na.rm = TRUE),
max =
max(IQA,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 27.8 34.6 40.0 39.5 43.5 48.7
## 2 87398900 28.5 35.1 37.6 38.3 40.6 48.5
## 3 87398950 21.1 29.4 32.7 32.8 36.8 44.0
## 4 87398980 24.5 35.7 39.4 39.5 43.4 52.1
## 5 87405500 19.8 28.7 31.5 31.9 35.7 48.8
## 6 87406900 17.1 25.3 29.0 29.5 32.8 44.1
## 7 87409900 16.2 20.5 26.1 25.0 29.8 33.1(sum_IQA_p3 <- plan_wide_19902020 %>%
select(CODIGO, IQA, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(IQA,
na.rm = TRUE),
q1 =
quantile(IQA, 0.25,
na.rm = TRUE),
median =
median(IQA,
na.rm = TRUE),
mean =
mean(IQA,
na.rm= TRUE),
q3 =
quantile(IQA, 0.75,
na.rm = TRUE),
max =
max(IQA,
na.rm = TRUE),
n =
length(IQA))
)## # A tibble: 7 x 8
## CODIGO min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 40.2 42.5 45.4 44.2 45.5 47.2 34
## 2 87398900 34.1 38.6 41.2 40.2 42.9 44.4 36
## 3 87398950 36.7 39.5 42.4 41.5 44.4 44.6 35
## 4 87398980 40.0 40.0 40.0 40.0 40.0 40.0 28
## 5 87405500 30.8 31.6 32.5 32.5 33.3 34.1 33
## 6 87406900 22.9 24.4 25.9 25.3 26.5 27.2 35
## 7 87409900 24.1 25.1 27.3 26.9 28.2 29.7 37plan_wide_19902020 %>%
select(CODIGO, IQA) %>%
group_by(CODIGO) %>%
summarize(
min =
min(IQA,
na.rm = TRUE),
q1 =
quantile(IQA, 0.25,
na.rm = TRUE),
median =
median(IQA,
na.rm = TRUE),
mean =
mean(IQA,
na.rm= TRUE),
q3 =
quantile(IQA, 0.75,
na.rm = TRUE),
max =
max(IQA,
na.rm = TRUE))## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 27.0 35.6 40.7 40.5 45.4 52.2
## 2 87398900 27.8 36.4 40.7 41.4 46.1 58.5
## 3 87398950 21.1 36.6 40.7 41.8 47.4 61.9
## 4 87398980 24.5 35.7 39.7 39.9 44.1 52.1
## 5 87405500 19.8 29.9 36.9 37.3 44.0 57.6
## 6 87406900 17.1 25.7 31.1 32.4 38.0 49.0
## 7 87409900 16.2 28.1 33.2 35.3 42.7 55.4ggsave("iqa_p1.png",
plot = iqa_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 164 rows containing missing values (position_quasirandom).ggsave("iqa_p2.png",
plot = iqa_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 253 rows containing missing values (position_quasirandom).ggsave("iqa_p3.png",
plot = iqa_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 214 rows containing missing values (position_quasirandom).ggsave("iqa_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 164 rows containing missing values (position_quasirandom).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 253 rows containing missing values (position_quasirandom).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 214 rows containing missing values (position_quasirandom).
## Using ragg device as default. Ignoring `type` and `antialias` arguments
7.1 Testando coisas
7.1.1 Correlação
parametros_IQA <- plan_wide_19902020 %>%
select(CODIGO,
pH,
DBO,
`Nitrogênio amoniacal`,
`Nitrogênio total`,
`Fósforo total`,
`Temperatura água`,
Turbidez,
`Sólidos totais`,
`Oxigênio dissolvido`,
Condutividade)
write.csv(parametros_IQA,
"./parametros_IQA.csv",
row.names = FALSE)
parametros_IQA %>%
select(-CODIGO) %>%
ggcorr(method = "complete.obs",
# "pearson",
# "pairwise",
name = "Correlação",
label = TRUE,
label_alpha = TRUE,
digits = 3,
low = "#3B9AB2",
mid = "#EEEEEE",
high = "#F21A00",
# palette = "RdYlBu",
layout.exp = 0,
legend.position = "left",
label_round = 3,
)
# Gráfico das correlações entre todos os parâmetros com significância
# correl_IQA <- parametros_IQA %>%
# select(-CODIGO) %>%
# ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
# axisLabels = "show")7.1.2 Condutividade elétrica
(cond_elet_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
Condutividade))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Condutividade elétrica no período 1990-2000",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme(
plot.title = element_text(
hjust = 0.5,
color = "black",
size = 19),
axis.title.y = element_text(
color = "black",
size = 15),
axis.text.y = element_text(
color = "black",
size = 17),
axis.text.x = element_text(
color = "black",
size = 17),
)
)
(cond_elet_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
Condutividade))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Condutividade elétrica no período 1990-2000",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme(
plot.title = element_text(
hjust = 0.5,
color = "black",
size = 19),
axis.title.y = element_text(
color = "black",
size = 15),
axis.text.y = element_text(
color = "black",
size = 17),
axis.text.x = element_text(
color = "black",
size = 17),
)
)
(cond_elet_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
Condutividade))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Condutividade elétrica no período 1990-2000",
x="Estação",
y="")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
scale_x_discrete(limits = c("87398500", "87398980", "87398900",
"87398950", "87405500", "87406900", "87409900"))+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme(
plot.title = element_text(
hjust = 0.5,
color = "black",
size = 19),
axis.title.y = element_text(
color = "black",
size = 15),
axis.text.y = element_text(
color = "black",
size = 17),
axis.text.x = element_text(
color = "black",
size = 17),
)
)
grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)
(sum_cond_elet_p1 <- plan_wide_19902020 %>%
select(CODIGO, Condutividade, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Condutividade,
na.rm = TRUE),
q1 =
quantile(Condutividade, 0.25,
na.rm = TRUE),
median =
median(Condutividade,
na.rm = TRUE),
mean =
mean(Condutividade,
na.rm= TRUE),
q3 =
quantile(Condutividade, 0.75,
na.rm = TRUE),
max =
max(Condutividade,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 9.4 51.1 67 75.1 83.2 340
## 2 87398900 10 41.5 51 55.3 64.2 160
## 3 87398950 9 41.5 51.5 60.1 69.5 160
## 4 87398980 11.3 42.4 52.0 53.0 67.0 83.8
## 5 87405500 25 68.7 88.2 130. 170 560
## 6 87406900 52 88.4 133. 193. 256. 576
## 7 87409900 29 80 110. 134. 168. 460(sum_cond_elet_p2 <- plan_wide_19902020 %>%
select(CODIGO, Condutividade, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Condutividade,
na.rm = TRUE),
q1 =
quantile(Condutividade, 0.25,
na.rm = TRUE),
median =
median(Condutividade,
na.rm = TRUE),
mean =
mean(Condutividade,
na.rm= TRUE),
q3 =
quantile(Condutividade, 0.75,
na.rm = TRUE),
max =
max(Condutividade,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 11.9 67.0 82.6 84.8 102. 164.
## 2 87398900 11 44.4 52.3 57.1 72.6 136.
## 3 87398950 39.8 58.4 76 82.3 98.3 160
## 4 87398980 9.4 42.4 49.7 51.5 62 114.
## 5 87405500 17 77.5 107 142. 171. 679
## 6 87406900 23.1 85.6 124. 164. 199. 619
## 7 87409900 56.1 114. 177 200. 242 454(sum_cond_elet_p3 <- plan_wide_19902020 %>%
select(CODIGO, Condutividade, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Condutividade,
na.rm = TRUE),
q1 =
quantile(Condutividade, 0.25,
na.rm = TRUE),
median =
median(Condutividade,
na.rm = TRUE),
mean =
mean(Condutividade,
na.rm= TRUE),
q3 =
quantile(Condutividade, 0.75,
na.rm = TRUE),
max =
max(Condutividade,
na.rm = TRUE),
n =
length(Condutividade))
)## # A tibble: 7 x 8
## CODIGO min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 0.01 68.5 80.2 80.4 99.5 125. 34
## 2 87398900 39.7 53.4 58.3 61.1 65.5 103 36
## 3 87398950 40.9 64.7 70.1 76.1 82.5 195. 35
## 4 87398980 43.2 51.7 54.0 56.3 61.0 78.9 28
## 5 87405500 47 85.8 121. 146. 209. 286 33
## 6 87406900 62.7 95.9 142. 163. 216. 354. 35
## 7 87409900 65.7 121. 159. 179. 245. 498. 37# plan_wide_19902020 %>%
# select(CODIGO, IQA) %>%
# group_by(CODIGO) %>%
# summarize(
# min =
# min(IQA,
# na.rm = TRUE),
# q1 =
# quantile(IQA, 0.25,
# na.rm = TRUE),
# median =
# median(IQA,
# na.rm = TRUE),
# mean =
# mean(IQA,
# na.rm= TRUE),
# q3 =
# quantile(IQA, 0.75,
# na.rm = TRUE),
# max =
# max(IQA,
# na.rm = TRUE))ggsave("cond_elet_p1.png",
plot = cond_elet_p1,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Removed 15 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing missing values (position_quasirandom).ggsave("cond_elet_p2.png",
plot = cond_elet_p2,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 37 rows containing non-finite values (stat_boxplot).
## Removed 37 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 37 rows containing missing values (position_quasirandom).ggsave("cond_elet_p3.png",
plot = cond_elet_p3,
path = "./graficos",
dpi = 300,
type = "cairo")## Saving 10 x 6.66 in image## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments## Warning: Removed 25 rows containing non-finite values (stat_boxplot).
## Removed 25 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing missing values (position_quasirandom).ggsave("cond_elet_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")## Warning: Removed 15 rows containing non-finite values (stat_boxplot).## Warning: Removed 15 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing missing values (position_quasirandom).## Warning: Removed 37 rows containing non-finite values (stat_boxplot).
## Removed 37 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 37 rows containing missing values (position_quasirandom).## Warning: Removed 25 rows containing non-finite values (stat_boxplot).
## Removed 25 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing missing values (position_quasirandom).## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
---
title: "TCC"
author: "Leonardo Fernandes Wink"
date: "`r format(Sys.time(), '%d/%m/%Y')`"
output:
  html_document: 
    highlight: haddock
    keep_md: yes
    number_sections: yes
    theme: flatly
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: no
    fig_width: 10
    fig_height: 6.66
    fig_caption: yes
    code_download: true
  pdf_document:
    toc: yes
  word_document: 
    toc: yes
    keep_md: yes
always_allow_html: yes
editor_options: 
  chunk_output_type: console
fig.align: center
---

```{r Rotina pra toda vez que abrir o documento, echo = FALSE}
# Abrir o GitHub Desktop
# Abrir o RStudio
# Verificar se estão 
```

# Anotações de coisas por fazer:

* Descobrir como colocar as estações no sentido correto montante -> jusante nos sumários
* not entirely necessary pq tenho os sumários mais aprimorados ainda no excel.

> 87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900

* ~~Aprender a segmentar o meu dataset por períodos~~
* ~~aprender a criar uma nova coluna com a segmentação dos períodos~~
* aprender a colocar a legenda dentro do gráfico
  * reduzir o tamanho da legenda
* ~~corrigir os valores 0 de IQA pra NA~~
* descobrir como conseguir a equação do lm
* ~~aprender a pivotar o sumário~~ -> meu sumário do google docs ta batendo direitinho com o do R
* descobrir se há outros TCCs com disponibilização de códigos
* correlação forte entre condutividade e Namon/Ptot/DBO



| 1990-2000 | 2000-2010 | 2010-2020 |
| :-------: | :-------: | :-------: |
| 1990-2000 | 2000-2010 | 2010-2020 |



# Instalar os pacotes
```{r instalar pacotes}
# install.packages(tidyverse)
```

## acessar os pacotes
```{r Acessar os pacotes, message = FALSE, warning = TRUE}
# library(readr)
# library(rmarkdown)
# # library(qboxplot)
# library(readxl)
# library(pillar)
# library(dplyr)
# library(tidyverse)
# library(gapminder)
# library(knitr)
# library(kableExtra)
# library(ggpubr)
# library(gridExtra)
# library(modelsummary)
# library(gtsummary)
# library(GGally)
pacman::p_load(readr, rmarkdown, readxl,
               pillar, dplyr, tidyverse,
               gapminder, knitr, kableExtra,
               gridExtra, #modelsummary, 
               gtsummary, ggplot2,
               ggbeeswarm, GGally)
# pacman::p_load(tibbletime)
```

```{cronometrando quanto tempo cada chunk leva}
knitr::knit_hooks$set(time_it = local({
  now <- NULL
  function(before, options) {
    if (before) {
      # record the current time before each chunk
      now <<- Sys.time()
    } else {
      # calculate the time difference after a chunk
      res <- difftime(Sys.time(), now)
      # return a character string to show the time
      paste("Time for this code chunk to run:", res)
    }
  }
}))

knitr::opts_chunk$set(time_it = TRUE)
```

## importando a planilha
```{r Importando a planilha, echo = FALSE, message = TRUE, warning = FALSE}
plan_wide_19902020 <- read_delim("https://raw.githubusercontent.com/leonardofwink/TCC_gh/main/plan_wide_19902020.tsv",
                                 delim = "\t", 
                                 escape_double = FALSE,
                                 col_types = cols(
                                   Alcalinidade = col_double(),
                                   CODIGO = col_character(), 
                                   COORD_GEO_LAT_GRAU = col_double(),
                                   COORD_GEO_LONG_GRAU = col_double(),
                                   DATA_COLETA = col_date(format = "%d/%m/%Y"),
                                   Nitrato = col_double(), 
                                   Nitrito = col_double(),
                                   SDT = col_double(), 
                                   SST = col_double(),
                                   `Vazao` = col_double(), 
                                   `Vazao rio` = col_double()
                                 ),
                                 locale = locale(
                                   date_names = "pt", 
                                   decimal_mark = ",",
                                   grouping_mark = ""
                                 ),
                                 trim_ws = TRUE
)

# teste[~'2000']
# 
# teste <- plan_wide_19902020 %>%
#   dplyr::filter(DATA_COLETA >= as.POSIXct("2010-01-01")) #this works
# 
# teste$DATA_COLETA <- as.POSIXct(teste$DATA_COLETA)
# 
# teste %>% 
#   dplyr::arrange(DATA_COLETA)
# teste %>% 
#   filter_time(time_formula = '2013-01-01' ~ '2020-12-31')
# 
# 
# typeof(teste$DATA_COLETA)
# 
#   as_tbl_time(plan_wide_19902020, index = DATA_COLETA)
# str(plan_wide_19902020$DATA_COLETA)
```

```{r Mostrando os dados, layout="l-body-outset", echo = FALSE}
# library(rmarkdown)
# kable(head(plan_wide_19902020),format = "html") %>% 
#   kable_styling(full_width = FALSE,
#                 bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

```{r Visualização da planilha importada, echo = FALSE}
paged_table(plan_wide_19902020,
           options = list(rows.print = 15,
                          cols.print = 10))
```

# data wrangling

```{r data wrangling}
plan_wide_19902020 <- plan_wide_19902020 %>% 
  mutate(IQA = ifelse(IQA == 0, NA, IQA))
```

```{r Códigos Git, echo = FALSE}
# cd myrepo
# ls
# head README.md
# git status
# git add README.md
# git commit -m "A commit from my local computer"
# 
# cd .. # voltar pro diretório acima
# rm -rf myrepo/ #remover/apagar a pasta myrepo
```

```{r Aprendendo Git, echo = FALSE}
# slides da bia que ajudam mt
# https://beatrizmilz.github.io/slidesR/git_rstudio/11-2021-ENCE.html#20
# aprendendo a sincronizar usando esse guia -> 
# https://happygitwithr-com.translate.goog/push-pull-github.html?_x_tr_sl=auto&_x_tr_tl=pt&_x_tr_hl=pt-BR
# library(usethis)
# usethis::create_github_token() criar um código pra acesso e sincronização between R e github

# gitcreds::gitcreds_set() 
# 
# use_git_config(user.name = "leonardofwink",
#                user.email = "leonardofwink@gmail.com")
# usethis::gh_token_help()

# Como mostrar os dados de um arquivo via Git/GitHub
# git clone https://github.com/leonardofwink/myrepo.git
# cd myrepo #acessa a pasta myrepo
# ls #lista os arquivos da pasta 
# head README.md #mostra as primeiras observações do arquivo

# Como mostrar os dados de um arquivo via R
# head(C:/Users/Léo/myrepo/README.md)

# Adicionar uma linha ao README.md e verificar se o Git percebe a mudança
# echo "A line I wrote on my local computer" >> README.md
# git status
## C:\Users\Léo\myrepo>git status
## On branch main
## Your branch is up to date with 'origin/main'.
## 
## Changes not staged for commit:
##   (use "git add <file>..." to update what will be committed)
##   (use "git restore <file>..." to discard changes in working directory)
##         **modified:   README.md**
## 
## no changes added to commit (use "git add" and/or "git commit -a")
```

# setting theme
```{r setting theme}
theme_grafs <- function(bg = "white", 
                        coloracao_letra = "black") {
  theme(
    plot.title = element_text(
      hjust = 0.5,
      color = coloracao_letra,
      size = 19),
    
    axis.title.x = 
      # element_text(
      # color = coloracao_letra,
      # size = 15,
      # angle = 0,),
      element_blank(),
    axis.title.y = element_text(
      color = coloracao_letra,
      size = 15,
      angle = 90),
    
    axis.text.x = element_text(
      color = coloracao_letra,
      size = 17),
    axis.text.y = element_text(
      color = coloracao_letra,
      size = 17,
      angle = 0),
    
    panel.background = element_rect(fill = bg),
    plot.background = element_rect(fill = bg),
    plot.margin = margin(l = 5, r = 10,
                         b = 5, t = 5)
  )
}
```

# setting periodos
```{r setting periodos}
# p1 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "1990" &
#            ANO_COLETA <= "2000")
# 
# p2 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "2000" &
#            ANO_COLETA <= "2010")
# 
# p3 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "2010" &
#            ANO_COLETA <= "2020")

# teste_all_periodos <- plan_wide_19902020 %>% 
#   filter(
#     between(ANO_COLETA, 1990, 2000)
#   )
```

# setting sumaries
```{r Sumários}
p1 <- plan_wide_19902020 %>% 
  filter(ANO_COLETA > "1990" &
           ANO_COLETA <= "2000")

p2 <- plan_wide_19902020 %>%
  filter(ANO_COLETA > "2000" &
         ANO_COLETA <= "2010")

p3 <- plan_wide_19902020 %>%
  filter(ANO_COLETA > "2010" &
         ANO_COLETA <= "2020")

  # periodo = c(p1 <- plan_wide_19902020 %>% 
  #   filter(ANO_COLETA > "1990" &
  #            ANO_COLETA <= "2000"),
  # 
  # p2 <- plan_wide_19902020 %>%
  #   filter(ANO_COLETA > "2000" &
  #            ANO_COLETA <= "2010"),
  # 
  # p3 <- plan_wide_19902020 %>%
  #   filter(ANO_COLETA > "2010" &
  #            ANO_COLETA <= "2020"))

# sumario <- function(parametros = parametros, periodo){
#   plan_wide_19902020 %>%
#    select(CODIGO, ., ANO_COLETA) %>% 
#    # filter(ANO_COLETA>"1990" &
#    #          ANO_COLETA<="2000") %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#      min = 
#        min(parametros, 
#            na.rm = TRUE),
#      q1 = 
#        quantile(parametros, 0.25, 
#                 na.rm = TRUE),
#      median = 
#        median(parametros, 
#               na.rm = TRUE),
#      mean = 
#        mean(parametros, 
#             na.rm= TRUE),
#      q3 = 
#        quantile(parametros, 0.75, 
#                 na.rm = TRUE),
#      max = 
#        max(parametros, 
#            na.rm = TRUE))
# }

# plan_wide_19902020 %>% 
#   sumario(parametros = DBO)

# sum_IQA_p1 <- plan_wide_19902020 %>%
#    select(CODIGO, IQA, ANO_COLETA) %>% 
#    filter(ANO_COLETA>"1990" &
#             ANO_COLETA<="2000") %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#      min = 
#        min(IQA, 
#            na.rm = TRUE),
#      q1 = 
#        quantile(IQA, 0.25, 
#                 na.rm = TRUE),
#      median = 
#        median(IQA, 
#               na.rm = TRUE),
#      mean = 
#        mean(IQA, 
#             na.rm= TRUE),
#      q3 = 
#        quantile(IQA, 0.75, 
#                 na.rm = TRUE),
#      max = 
#        max(IQA, 
#            na.rm = TRUE))
```


# Parâmetros físico-químicos

### Oxigênio Dissolvido

```{r setting od}
# par_od <- plan_wide_19902020 %>% 
#   select(CODIGO, `Oxigênio dissolvido`) %>% 
#   group_nest(CODIGO)

# parametros_IQA

# parametros <- colnames(parametros_IQA)[]

plan_wide_19902020 %>%
  ggplot(
    aes(CODIGO, `Oxigênio dissolvido`)
  )+
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = -Inf, ymax = 2,
           alpha = 1,
           fill = "#ac5079")+ # >pior classe
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = 2, ymax = 4,
           alpha = 1,
           fill = "#eb5661")+ #classe 4
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = 4, ymax = 5,
           alpha=1,
           fill="#fcf7ab")+ #classe 3
  annotate("rect",
           xmin=-Inf,
           xmax=Inf,
           ymin=5,
           ymax=6,
           alpha=1,
           fill="#70c18c")+ #classe 2
  annotate("rect",
           xmin=-Inf,
           xmax=Inf,
           ymin=6,
           ymax=Inf,
           alpha=1,
           fill="#8dcdeb")+ #classe 1
  stat_boxplot(
    geom = 'errorbar',
    width=0.3,
    position = position_dodge(width = 0.65)
  )+
  geom_boxplot(
    fill = '#F8F8FF',
    color = "black",
    outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    width= 0.7
  )+
  labs(
    title = "Oxigênio Dissolvido no período 1990-2000",
    x = "Estação",
    y = "mg/L"
  )+
  ggbeeswarm::geom_quasirandom(
    size = 1.2,
    alpha = .25,
    width = .07,
  )+
  scale_y_continuous(
    expand = expansion(mult = c(0,0)),
    n.breaks = 11,
    limits = c(-1,21)
  )+
  scale_x_discrete(limits = c("87398500",
                              "87398980",
                              "87398900",
                              "87398950",
                              "87405500",
                              "87406900",
                              "87409900"),
                   labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
  )+
  geom_smooth(method = "lm",
              se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
              aes(group=1),
              alpha=.5,
              na.rm = TRUE,
              size = 1)



```


```{r Gráfico OD periodo 1, echo = FALSE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2000"}
(od_p1 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "1990" &
                          ANO_COLETA <= "2000"),
                 aes(CODIGO,
                     `Oxigênio dissolvido`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=2,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=2,
            ymax=4,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=4,
            ymax=5,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=6,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Oxigênio Dissolvido no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(
     expand = expansion(mult = c(0,0)),
     n.breaks = 11,
     limits = c(-1,21))+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(
     method = "lm",
     se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
     aes(group=1),
     alpha=.5,
     na.rm = TRUE,
     size = 1
   )+
  theme_grafs()
)
```

```{r Gráfico OD periodo 2, echo = FALSE, warning=FALSE, message = FALSE}
(od_p2 <-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"2000" &
                           ANO_COLETA<="2010"),
                aes(CODIGO,
                    `Oxigênio dissolvido`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=2,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=2,
            ymax=4,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=4,
            ymax=5,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=6,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Oxigênio Dissolvido no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 11,
                      limits = c(-1,21))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico OD periodo 3, echo = FALSE, warning=FALSE, message = FALSE}
(od_p3 <-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"2010" &
                           ANO_COLETA<="2020"),
                aes(CODIGO,
                    `Oxigênio dissolvido`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=2,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=2,
            ymax=4,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=4,
            ymax=5,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=6,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Oxigênio Dissolvido no período 2010-2020",
        x="",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 11,
                      limits = c(-1,21))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico OD 3 periodos juntos, echo = TRUE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2020"}
grid.arrange(od_p1, od_p2, od_p3, ncol = 3)
```

```{r Salvando OD}
ggsave("od_p1.png",
       plot = od_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p2.png",
       plot = od_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p3.png",
       plot = od_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_3periodos_2.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(od_p1, od_p2, od_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

```{r Gráfico OD_chernobyl, echo = FALSE, warning=FALSE, message = FALSE}
# p1 <- function(plan_wide_19902020, ANO_COLETA) {
#   plan_wide_19902020 %>% 
#     filter(ANO_COLETA > "1990" &
#            ANO_COLETA <= "2000")
# }
# 
# 
# classes_od <- function(plan_wide_19902020, parametro, periodo){
#   ggplot(plan_wide_19902020 %>%
#            periodo),
#   aes(CODIGO,
#       parametro)
# }


# (od_chernobyl <- ggplot(plan_wide_19902020 %>%
#                           p1(ANO_COLETA > "1990" &
#                                ANO_COLETA <= "2000"),
#                         aes(CODIGO,
#                             `Oxigênio dissolvido`))+
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=-Inf,
#              ymax=2,
#              alpha=1,
#              fill="#ac5079")+ #>pior classe
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=2,
#              ymax=4,
#              alpha=1,
#              fill="#eb5661")+ #classe 4
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=4,
#              ymax=5,
#              alpha=1,
#              fill="#fcf7ab")+ #classe 3
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=5,
#              ymax=6,
#              alpha=1,
#              fill="#70c18c")+ #classe 2
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=6,
#              ymax=Inf,
#              alpha=1,
#              fill="#8dcdeb")+ #classe 1
#     stat_boxplot(geom = 'errorbar',
#                  width=0.3,
#                  position = position_dodge(width = 0.65))+
#     geom_boxplot(fill='#F8F8FF',
#                  color="black",
#                  outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
#                  width= 0.7)+
#     labs(title = "Oxigênio Dissolvido no período 1990-2000",
#          x="Estação",
#          y="mg/L")+
#     # geom_jitter(width = .07,
#     #             alpha=.15,
#     #             size=1.,
#     #             color="black")+
#     ggbeeswarm::geom_quasirandom(
#       size = 1.2,
#       alpha = .25,
#       width = .07,
#     )+
#     scale_y_continuous(expand = expansion(mult = c(0,0)),
#                        n.breaks = 11,
#                        limits = c(-1,21))+
#     scale_x_discrete(limits = c("87398500",
#                                 "87398980",
#                                 "87398900",
#                                 "87398950",
#                                 "87405500",
#                                 "87406900",
#                                 "87409900"),
#                      labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
#     )+
#     geom_smooth(method = "lm",
#                 se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
#                 aes(group=1),
#                 alpha=.5,
#                 na.rm = TRUE,
#                 size = 1)+
#     # geom_line(
#     #   aes(color="red"),
#     #   alpha=.0)+
#     # scale_color_manual("Legenda",
#     #                    guide="legend",
#     #                    values = c("Classe 1"="#8dcdeb",
#     #                               "Classe 2"="#70c18c",
#     #                               "Classe 3"="#fcf7ab",
#     #                               "Classe 4"="#eb5661",
#     #                               "Pior Classe"="#ac5079"))+
#     # guides(color=guide_legend(override.aes = list(linetype=c(1,1,1,1,1),
#   #                                               lwd=c(2,2,2,2,2),
#   #                                               shape=c(NA,NA,NA,NA,NA),
#   #                                               alpha=1)))+
#   theme(
#     plot.title = element_text(size = 19),
#     axis.title.y = element_text(size = 15),
#     axis.text.y = element_text(size = 17),
#     axis.text.x = element_text(size = 17),
#   )
# )
```

```{r mais um teste, echo = FALSE, warning=FALSE, message = FALSE}
# install.packages("modelsummary")
# library(modelsummary)
# url111 <- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
# dat <- read.csv(url111)
# dat$Small <- dat$Pop1831 > median(dat$Pop1831)
# datasummary_skim(dat)
# datasummary_balance(~Small, dat)
# datasummary_correlation(dat)
# 
# 
# datasummary_correlation(parametros_IQA)
# datasummary(Literacy + Commerce~Small *(mean+sd), dat)
# mod <- lm(Donations~Crime_prop, data = dat)
# 
# mod2 <-lm(data = plan_wide_19902020,
#           plan_wide_19902020$CODIGO ~ list(parametros_IQA))
# 
# for (i in 1:lenght(parametros_IQA)){
#   lm(plan_wide_19902020$CODIGO ~ parametros_IQA[i])
# }
# 
# training_data <- as.data.frame(matrix(sample(1:64), nrow = 8))
# colnames(training_data) <- c("independent_variable", paste0("x", 1:7))
# Vars <- as.list(c("x1+x2+x3",
#                 "x1+x2+x4",
#                 "x1+x2+x5",
#                 "x1+x2+x6",
#                 "x1+x2+x7"))
# allModelsList <- lapply(paste("independent_variable ~", Vars), as.formula)
# allModelsResults <- lapply(allModelsList, function(x) lm(x, data = training_data))
# allModelsSummaries = lapply(allModelsResults, summary) 
# allModelsSummaries[[1]]$r.squared
# 
# 
# 
# 
# 
# modelsummary(mod)
# 
# plan_wide_19902020 %>%
#   select(CODIGO,
#          DBO,
#          `Oxigênio dissolvido`,
#          `Fósforo total`,
#          `Escherichia coli`,
#          `Nitrogênio amoniacal`,
#          pH,
#          Turbidez,
#          `Sólidos totais`,
#          `Temperatura água`) %>%
#   datasummary_skim()
# 
# plan_wide_19902020 %>%
#   select(CODIGO,
#          `Oxigênio dissolvido`) %>%
#   datasummary_skim()

# install.packages("estimatr")
# library(estimatr)

# glimpse(plan_wide_19902020)


# datasummary(
#   unique(CODIGO) + `Oxigênio dissolvido` ~
#     P25 + Mean,data = plan_wide_19902020 %>%
#     select(CODIGO,
#            `Oxigênio dissolvido`)
# )
# install.packages("gtsummary", type = "binary")
# library(gtsummary)



# parametros_IQA <- plan_wide_19902020 %>%
#   select(CODIGO,
#          `Escherichia coli`,
#          pH,
#          DBO,
#          `Nitrogênio amoniacal`,
#          `Nitrogênio total`,
#          `Fósforo total`,
#          `Temperatura água`,
#          Turbidez,
#          `Sólidos totais`,
#          `Oxigênio dissolvido`,
#          Condutividade)
# 
# 
# mod <- lm(y ~ x, cars)
# modelsummary(cars)

# fit<-lm(plan_wide_19902020$CODIGO~plan_wide_19902020$DBO)
# summary(fit)
# ggplot(plan_wide_19902020,
#        aes(x=CODIGO,
#            y=DBO,
#            add = "reg.line"))+
#   geom_smooth(method = "lm",
#                se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
#                aes(group = 1),
#                alpha= .8,
#                na.rm = TRUE,
#                size = 1)+
#   geom_point(na.rm = TRUE)+
#   stat_regline_equation(aes(label = ..eq.label..),
#                         na.rm = TRUE,
#                         label.x.npc = "center",
#                         label.y.npc = "center",
#                         geom = "text")
#   
# 
# summary(fit)
```

```{r Gráfico IQA OD periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqaod_p1 <-ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA > "1990" &
                              ANO_COLETA <= "2000"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs())
```

```{r Gráfico IQA OD periodo2, echo = FALSE, warning= FALSE, message = FALSE}
(iqaod_p2 <-ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA > "2000" &
                              ANO_COLETA <= "2010"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs())

```

```{r Gráfico IQA OD periodo3, echo = FALSE, warning=FALSE, message = FALSE}
(iqaod_p3 <-ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA > "2010" &
                              ANO_COLETA <= "2020"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)), #expandir o limite max de visualização do dado em x%
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs())
```

```{r Gráfico OD_IQA 6 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(iqaod_p1, iqaod_p2, iqaod_p3, ncol = 3)
```

Resultado que quero chegar pro sumário

| variable  | Estação 1 | Estação 2 |  Estação 3 |  Estação 4 |
| :-------: | :-------: | :-------: |  :-------: |  :-------: |
| max       | 15        |   17      |     16     |    14      |
| med       | 14        |   16      |     15     |    13      |
| min       | 13        |   15      |     14     |    12      |
| n         | 15        |   12      |     3      |    4       |

```{r Sumário OD, echo = FALSE}
plan_wide_19902020 %>%
  select(CODIGO, `Oxigênio dissolvido`, ANO_COLETA) %>% 
  filter(ANO_COLETA>"1990" &
           ANO_COLETA<="2000") %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(`Oxigênio dissolvido`, na.rm = TRUE),
    q1 = 
      quantile(`Oxigênio dissolvido`, 0.25, na.rm = TRUE),
    median = 
      median(`Oxigênio dissolvido`, na.rm = TRUE),
    mean = 
      mean(`Oxigênio dissolvido`, na.rm= TRUE),
    q3 = 
      quantile(`Oxigênio dissolvido`, 0.75, na.rm = TRUE),
    max = 
      max(`Oxigênio dissolvido`, na.rm = TRUE))

plan_wide_19902020 %>%
  select(CODIGO, `Oxigênio dissolvido`, ANO_COLETA) %>% 
  filter(ANO_COLETA>"2000" &
           ANO_COLETA<="2010") %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(`Oxigênio dissolvido`, na.rm = TRUE),
    q1 = 
      quantile(`Oxigênio dissolvido`, 0.25, na.rm = TRUE),
    median = 
      median(`Oxigênio dissolvido`, na.rm = TRUE),
    mean = 
      mean(`Oxigênio dissolvido`, na.rm= TRUE),
    q3 = 
      quantile(`Oxigênio dissolvido`, 0.75, na.rm = TRUE),
    max = 
      max(`Oxigênio dissolvido`, na.rm = TRUE))

plan_wide_19902020 %>%
  select(CODIGO, `Oxigênio dissolvido`, ANO_COLETA) %>% 
  filter(ANO_COLETA>"2010" &
           ANO_COLETA<="2020") %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(`Oxigênio dissolvido`, na.rm = TRUE),
    q1 = 
      quantile(`Oxigênio dissolvido`, 0.25, na.rm = TRUE),
    median = 
      median(`Oxigênio dissolvido`, na.rm = TRUE),
    mean = 
      mean(`Oxigênio dissolvido`, na.rm= TRUE),
    q3 = 
      quantile(`Oxigênio dissolvido`, 0.75, na.rm = TRUE),
    max = 
      max(`Oxigênio dissolvido`, na.rm = TRUE))

# sumario_OD3 <- plan_wide_19902020 %>%
#   select(DATA_COLETA, CODIGO, `Oxigênio dissolvido`) %>% 
#   pivot_wider(id_cols = DATA_COLETA,
#               names_from = CODIGO,
#               values_from = plan_wide_19902020$`Oxigênio dissolvido`)
# 
# unique(plan_wide_19902020$CODIGO)

# 
#   pivot_wider(id_cols = CODIGO,
#               names_from = CODIGO,
#               values_from = `Oxigênio dissolvido`)
# 
# 
#   group_by(CODIGO) %>%
#   get_summary_stats(type = "common") %>%
#   pivot_wider(id_cols = variable,
#               names_from = CODIGO,
#               values_from = variable$`Oxigênio dissolvido`)
# 
# # install.packages("ggpubr")
# # library(ggpubr)
```

```{r setup, include=FALSE}
# knitr::opts_chunk$set(echo = TRUE)
```

### Demanda Bioquímica de Oxigênio

```{r Gráfico DBO período1, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p1<-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"1990" &
                           ANO_COLETA<="2000"),
                aes(CODIGO,
                    DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico DBO período2, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p2<-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"2000" &
                           ANO_COLETA<="2010"),
                aes(CODIGO,
                    DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico DBO período3, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p3<-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"2010" &
                           ANO_COLETA<="2020"),
                aes(CODIGO,
                    DBO, na.rm=TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico IQA DBO periodo1, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo1<-ggplot(plan_wide_19902020 %>% 
                    filter(ANO_COLETA>"1990" &
                             ANO_COLETA<="2000"),
                  aes(CODIGO,
                      IQA_DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 1990-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico IQA DBO periodo2, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo2<-ggplot(plan_wide_19902020%>% 
                    filter(ANO_COLETA>"2000" &
                             ANO_COLETA<="2010"),
                  aes(CODIGO,
                      IQA_DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950",
                               "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
 )
```

```{r Gráfico IQA DBO periodo3, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo3<-ggplot(plan_wide_19902020%>% 
                    filter(ANO_COLETA>"2010" &
                             ANO_COLETA<="2020"),
                  aes(CODIGO,
                      IQA_DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
 )
```

```{r Gráfico DBO 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3)
```

```{r Sumário DBO}
(sum_dbo_p1 <- plan_wide_19902020 %>%
   select(CODIGO, DBO, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(DBO, 
           na.rm = TRUE),
     q1 = 
       quantile(DBO, 0.25, 
                na.rm = TRUE),
     median = 
       median(DBO, 
              na.rm = TRUE),
     mean = 
       mean(DBO, 
            na.rm= TRUE),
     q3 = 
       quantile(DBO, 0.75, 
                na.rm = TRUE),
     max = 
       max(DBO, 
           na.rm = TRUE))
)

(sum_dbo_p2 <- plan_wide_19902020 %>%
    select(CODIGO, DBO, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(DBO, 
            na.rm = TRUE),
      q1 = 
        quantile(DBO, 0.25, 
                 na.rm = TRUE),
      median = 
        median(DBO, 
               na.rm = TRUE),
      mean = 
        mean(DBO, 
             na.rm= TRUE),
      q3 = 
        quantile(DBO, 0.75, 
                 na.rm = TRUE),
      max = 
        max(DBO, 
            na.rm = TRUE))
)

(sum_dbo_p3 <- plan_wide_19902020 %>%
    select(CODIGO, DBO, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(DBO, 
            na.rm = TRUE),
      q1 = 
        quantile(DBO, 0.25, 
                 na.rm = TRUE),
      median = 
        median(DBO, 
               na.rm = TRUE),
      mean = 
        mean(DBO, 
             na.rm= TRUE),
      q3 = 
        quantile(DBO, 0.75, 
                 na.rm = TRUE),
      max = 
        max(DBO, 
            na.rm = TRUE))
)
```

```{r Salvando DBO}
ggsave("dbo_p1.png",
       plot = dbo_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")


ggsave("dbo_p2.png",
       plot = dbo_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p3.png",
       plot = dbo_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```
### Fósforo total

```{r Gráfico Fósforo total periodo1, warning = FALSE, message = FALSE}
(ptot_p1<-ggplot(plan_wide_19902020%>% 
                   filter(ANO_COLETA>"1990" &
                            ANO_COLETA<="2000"),
                 aes(CODIGO,
                     `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)

```

```{r Gráfico Fósforo total periodo2, warning = FALSE, message = FALSE}
(ptot_p2 <- ggplot(plan_wide_19902020%>% 
                     filter(ANO_COLETA>"2000" &
                              ANO_COLETA<="2010"),
                   aes(CODIGO,
                       `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)

```

```{r Gráfico Fósforo total periodo3, warning = FALSE, message = FALSE}
(ptot_p3 <- ggplot(plan_wide_19902020%>% 
                     filter(ANO_COLETA>"2010" &
                              ANO_COLETA<="2020"),
                   aes(CODIGO,
                       `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

```

```{r Gráfico Ptot 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3)
```

```{r Sumário Fósforo total}
(sum_ptot_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Fósforo total`, na.rm = TRUE),
     q1 = 
       quantile(`Fósforo total`, 0.25, na.rm = TRUE),
     median = 
       median(`Fósforo total`, na.rm = TRUE),
     mean = 
       mean(`Fósforo total`, na.rm= TRUE),
     q3 = 
       quantile(`Fósforo total`, 0.75, na.rm = TRUE),
     max = 
       max(`Fósforo total`, na.rm = TRUE)))

(sum_ptot_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Fósforo total`, na.rm = TRUE),
      q1 = 
        quantile(`Fósforo total`, 0.25, na.rm = TRUE),
      median = 
        median(`Fósforo total`, na.rm = TRUE),
      mean = 
        mean(`Fósforo total`, na.rm= TRUE),
      q3 = 
        quantile(`Fósforo total`, 0.75, na.rm = TRUE),
      max = 
        max(`Fósforo total`, na.rm = TRUE)))

(sum_ptot_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Fósforo total`, na.rm = TRUE),
      q1 = 
        quantile(`Fósforo total`, 0.25, na.rm = TRUE),
      median = 
        median(`Fósforo total`, na.rm = TRUE),
      mean = 
        mean(`Fósforo total`, na.rm= TRUE),
      q3 = 
        quantile(`Fósforo total`, 0.75, na.rm = TRUE),
      max = 
        max(`Fósforo total`, na.rm = TRUE)))

```

```{r Salvando Ptot}
ggsave("ptot_p1.png",
       plot = ptot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p2.png",
       plot = ptot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p3.png",
       plot = ptot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Escherichia coli

```{r Gráfico Ecoli periodo1, warning = FALSE, message = FALSE}
(ecoli_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"1990" &
                               ANO_COLETA<="2000"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 1990-2000",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Ecoli periodo2, warning = FALSE, message = FALSE}
(ecoli_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2000" &
                               ANO_COLETA<="2010"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 2000-2010",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Ecoli periodo3, warning = FALSE, message = FALSE}
(ecoli_p3 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2010" &
                               ANO_COLETA<="2020"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 2010-2020",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico ecoli 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3)
```

```{r Sumário Ecoli}
(sum_ecoli_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Escherichia coli`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Escherichia coli`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Escherichia coli`, 
              na.rm = TRUE),
     mean = 
       mean(`Escherichia coli`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Escherichia coli`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Escherichia coli`, 
           na.rm = TRUE))
)

(sum_ecoli_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Escherichia coli`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Escherichia coli`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Escherichia coli`, 
               na.rm = TRUE),
      mean = 
        mean(`Escherichia coli`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Escherichia coli`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Escherichia coli`, 
            na.rm = TRUE))
)

(sum_ecoli_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Escherichia coli`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Escherichia coli`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Escherichia coli`, 
               na.rm = TRUE),
      mean = 
        mean(`Escherichia coli`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Escherichia coli`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Escherichia coli`, 
            na.rm = TRUE))
)
```

```{r Salvando ecoli}
ggsave("ecoli_p1.png",
       plot = ecoli_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p2.png",
       plot = ecoli_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p3.png",
       plot = ecoli_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```
### Nitrogênio amoniacal

```{r Gráfico Nitrogênio total periodo1, warning = FALSE, message = FALSE}
(namon_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"1990" &
                               ANO_COLETA<="2000"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio total periodo2, warning = FALSE, message = FALSE}
(namon_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2000" &
                               ANO_COLETA<="2010"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio total periodo3, warning = FALSE, message = FALSE}
(namon_p3 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2010" &
                               ANO_COLETA<="2020"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Namon 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)
```

```{r Sumário Nitrogênio total}
(sum_namon_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Nitrogênio total`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Nitrogênio total`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Nitrogênio total`, 
              na.rm = TRUE),
     mean = 
       mean(`Nitrogênio total`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Nitrogênio total`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Nitrogênio total`, 
           na.rm = TRUE))
)

(sum_namon_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Nitrogênio total`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Nitrogênio total`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Nitrogênio total`, 
               na.rm = TRUE),
      mean = 
        mean(`Nitrogênio total`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Nitrogênio total`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Nitrogênio total`, 
            na.rm = TRUE))
)

(sum_namon_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Nitrogênio total`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Nitrogênio total`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Nitrogênio total`, 
               na.rm = TRUE),
      mean = 
        mean(`Nitrogênio total`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Nitrogênio total`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Nitrogênio total`, 
            na.rm = TRUE))
)
```

```{r Salvando namon}
ggsave("namon_p1.png",
       plot = namon_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p2.png",
       plot = namon_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p3.png",
       plot = namon_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Turbidez
```{r Gráfico Turbidez periodo1, warning = FALSE, message = FALSE}
(turb_p1 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"1990" &
                              ANO_COLETA<="2000"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 1990-2000",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez periodo2, warning = FALSE, message = FALSE}
(turb_p2 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"2000" &
                              ANO_COLETA<="2010"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2000-2010",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez periodo3, warning = FALSE, message = FALSE}
(turb_p3 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"2010" &
                              ANO_COLETA<="2020"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2010-2020",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico turb 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)
```

```{r Sumário Turbidez}
(sum_turb_p1 <- plan_wide_19902020 %>%
   select(CODIGO, Turbidez, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(Turbidez, 
           na.rm = TRUE),
     q1 = 
       quantile(Turbidez, 0.25, 
                na.rm = TRUE),
     median = 
       median(Turbidez, 
              na.rm = TRUE),
     mean = 
       mean(Turbidez, 
            na.rm= TRUE),
     q3 = 
       quantile(Turbidez, 0.75, 
                na.rm = TRUE),
     max = 
       max(Turbidez, 
           na.rm = TRUE))
)

(sum_turb_p2 <- plan_wide_19902020 %>%
    select(CODIGO, Turbidez, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(Turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Turbidez, 
               na.rm = TRUE),
      mean = 
        mean(Turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(Turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Turbidez, 
            na.rm = TRUE))
)

(sum_turb_p3 <- plan_wide_19902020 %>%
    select(CODIGO, Turbidez, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(Turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Turbidez, 
               na.rm = TRUE),
      mean = 
        mean(Turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(Turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Turbidez, 
            na.rm = TRUE))
) 
```

```{r Salvando turb}
ggsave("turb_p1.png",
       plot = turb_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p2.png",
       plot = turb_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p3.png",
       plot = turb_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### pH
```{r Gráfico pH periodo1, warning = FALSE, message = FALSE}
(pH_p1 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"1990" &
                            ANO_COLETA<="2000"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo2, warning = FALSE, message = FALSE}
(pH_p2 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2000" &
                            ANO_COLETA<="2010"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo3, warning = FALSE, message = FALSE}
(pH_p3 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2010" &
                            ANO_COLETA<="2020"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)
```

```{r Sumário pH}
(sum_pH_p1 <- plan_wide_19902020 %>%
   select(CODIGO, pH, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(pH, 
           na.rm = TRUE),
     q1 = 
       quantile(pH, 0.25, 
                na.rm = TRUE),
     median = 
       median(pH, 
              na.rm = TRUE),
     mean = 
       mean(pH, 
            na.rm= TRUE),
     q3 = 
       quantile(pH, 0.75, 
                na.rm = TRUE),
     max = 
       max(pH, 
           na.rm = TRUE))
)

(sum_pH_p2 <- plan_wide_19902020 %>%
    select(CODIGO, pH, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
) 

(sum_pH_p3 <- plan_wide_19902020 %>%
    select(CODIGO, pH, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
)
```

```{r Salvando pH}
ggsave("pH_p1.png",
       plot = pH_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p2.png",
       plot = pH_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p3.png",
       plot = pH_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Sólidos totais

```{r Gráfico SólTot periodo1, warning = FALSE, message = FALSE}
(SolTot_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"1990" &
                                ANO_COLETA<="2000"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot periodo2, warning = FALSE, message = FALSE}
(SolTot_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2000" &
                                ANO_COLETA<="2010"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot periodo3, warning = FALSE, message = FALSE}
(SolTot_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2010" &
                                ANO_COLETA<="2020"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)
```

```{r Sumário Sólidos Totais}
(sum_SolTot_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Sólidos totais`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Sólidos totais`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Sólidos totais`, 
              na.rm = TRUE),
     mean = 
       mean(`Sólidos totais`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Sólidos totais`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Sólidos totais`, 
           na.rm = TRUE))
)

(sum_SolTot_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Sólidos totais`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Sólidos totais`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Sólidos totais`, 
               na.rm = TRUE),
      mean = 
        mean(`Sólidos totais`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Sólidos totais`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Sólidos totais`, 
            na.rm = TRUE))
)

(sum_SolTot_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Sólidos totais`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Sólidos totais`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Sólidos totais`, 
               na.rm = TRUE),
      mean = 
        mean(`Sólidos totais`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Sólidos totais`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Sólidos totais`, 
            na.rm = TRUE))
)
```

```{r Salvando SolTot}
ggsave("SolTot_p1.png",
       plot = SolTot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p2.png",
       plot = SolTot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p3.png",
       plot = SolTot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### IQA

```{r Gráfico IQA periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p1 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "1990" &
                            ANO_COLETA <= "2000"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA no período 1990-2000",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                                "87398950", "87405500", "87406900", "87409900"))+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
   theme_grafs()+
   theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA periodo2, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p2 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "2000" &
                            ANO_COLETA <= "2010"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()+
   theme(axis.title.y = element_blank()
   )
)
```

```{r Gráfico IQA periodo3, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p3 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "2010" &
                            ANO_COLETA <= "2020"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()+
   theme(axis.title.y = element_blank())
 )
```

```{r Gráfico IQA 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)
```

```{r Sumário IQA}
(sum_IQA_p1 <- plan_wide_19902020 %>%
   select(CODIGO, IQA, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(IQA, 
           na.rm = TRUE),
     q1 = 
       quantile(IQA, 0.25, 
                na.rm = TRUE),
     median = 
       median(IQA, 
              na.rm = TRUE),
     mean = 
       mean(IQA, 
            na.rm= TRUE),
     q3 = 
       quantile(IQA, 0.75, 
                na.rm = TRUE),
     max = 
       max(IQA, 
           na.rm = TRUE))
)

(sum_IQA_p2 <- plan_wide_19902020 %>%
    select(CODIGO, IQA, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(IQA, 
            na.rm = TRUE),
      q1 = 
        quantile(IQA, 0.25, 
                 na.rm = TRUE),
      median = 
        median(IQA, 
               na.rm = TRUE),
      mean = 
        mean(IQA, 
             na.rm= TRUE),
      q3 = 
        quantile(IQA, 0.75, 
                 na.rm = TRUE),
      max = 
        max(IQA, 
            na.rm = TRUE))
)

(sum_IQA_p3 <- plan_wide_19902020 %>%
    select(CODIGO, IQA, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(IQA, 
            na.rm = TRUE),
      q1 = 
        quantile(IQA, 0.25, 
                 na.rm = TRUE),
      median = 
        median(IQA, 
               na.rm = TRUE),
      mean = 
        mean(IQA, 
             na.rm= TRUE),
      q3 = 
        quantile(IQA, 0.75, 
                 na.rm = TRUE),
      max = 
        max(IQA, 
            na.rm = TRUE),
      n = 
        length(IQA))
)

plan_wide_19902020 %>% 
  select(CODIGO, IQA) %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(IQA, 
          na.rm = TRUE),
    q1 = 
      quantile(IQA, 0.25, 
               na.rm = TRUE),
    median = 
      median(IQA, 
             na.rm = TRUE),
    mean = 
      mean(IQA, 
           na.rm= TRUE),
    q3 = 
      quantile(IQA, 0.75, 
               na.rm = TRUE),
    max = 
      max(IQA, 
          na.rm = TRUE))
```

```{r Salvando iqa}
ggsave("iqa_p1.png",
       plot = iqa_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p2.png",
       plot = iqa_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p3.png",
       plot = iqa_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

## Testando coisas
```{r Testando coisas, include = FALSE}
# plan_wide_19902020 %>% 
#    select(CODIGO, `Oxigênio dissolvido`, ANO_COLETA) %>% 
#    ggplot(aes(ANO_COLETA, `Oxigênio dissolvido`, 
#       col = CODIGO))+
#    geom_line()+
#    facet_wrap(~ CODIGO, ncol = 7)

# df111 <- data.frame(x = c(1:100))
# glimpse(df111)
# df111$y <- 2 + 3 * df111$x + rnorm(100, sd = 40)
# 
# lm_eqn <- function(df111){
#     m <- lm(y ~ x, df111);
#     eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#          list(a = format(unname(coef(m)[1]), digits = 2),
#               b = format(unname(coef(m)[2]), digits = 2),
#              r2 = format(summary(m)$r.squared, digits = 3)))
#     as.character(as.expression(eq));
# } 
# p2 <- p111 +
#   geom_text(x = 25, y = 300,
#             label = lm_eqn(df111),
#             parse = TRUE)
# p2
# 
# 
# lm_eqc <- function(plan_wide_19902020){
#    m <- lm(`Oxigênio dissolvido` ~ CODIGO, plan_wide_19902020);
#    eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#                     list(a = format(unname(coef(m)[1]), digits = 2),
#                          b = format(unname(coef(m)[2]), digits = 2),
#                          r2 = format(summary(m)$r.squared, digits = 3)))
#    as.character(as.expression(eq));
# }
# 
# (od_p1 <-ggplot(plan_wide_19902020 %>%
#                    filter(ANO_COLETA>"1990" &
#                              ANO_COLETA<="2000"),
#                 aes(CODIGO,
#                     `Oxigênio dissolvido`))+
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=-Inf,
#                ymax=2,
#                alpha=1,
#                fill="#ac5079")+ #>pior classe
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=2,
#                ymax=4,
#                alpha=1,
#                fill="#eb5661")+ #classe 4
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=4,
#                ymax=5,
#                alpha=1,
#                fill="#fcf7ab")+ #classe 3
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=5,
#                ymax=6,
#                alpha=1,
#                fill="#70c18c")+ #classe 2
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=6,
#                ymax=Inf,
#                alpha=1,
#                fill="#8dcdeb")+ #classe 1
#       stat_boxplot(geom = 'errorbar',
#                    width=0.3,
#                    position = position_dodge(width = 0.65))+
#       geom_boxplot(fill='#F8F8FF',
#                    color="black",
#                    outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
#                    width= 0.7)+
#       labs(title = "Oxigênio Dissolvido no período 1990-2000",
#            x="Estação",
#            y="mg/L")+
#       # geom_jitter(width = .05,
#       #             alpha=.2,
#       #             size=1.5,
#       #             color="black")+
#       scale_y_continuous(expand = expansion(mult = c(0,0)),
#                          n.breaks = 11,
#                          limits = c(-1,21))+
#       scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
#       geom_smooth(method = "lm",
#                   se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
#                   aes(group=1),
#                   alpha=.5,
#                   na.rm = TRUE,
#                   size = 1)+
#       # annotate(geom_text(aes(x = "87405500", y = 15),
#       #                    label = lm_eqc(plan_wide_19902020),
#       #                    parse = TRUE,
#       #                    inherit.aes = TRUE,
#       #                    check_overlap = TRUE))+
#       #  geom_line(
#       #     aes(color="red"),
#       #     alpha=.0,
#       # )+
#       # scale_color_manual("Legenda",
#       #                    guide="legend",
#       #                    values = c("Classe 1"="#8dcdeb",
#       #                               "Classe 2"="#70c18c",
#       #                               "Classe 3"="#fcf7ab",
#       #                               "Classe 4"="#eb5661",
#       #                               "Pior Classe"="#ac5079"))+
#    # guides(color=guide_legend(override.aes = list(linetype=c(1,1,1,1,1),
#    #                                               lwd=c(2,2,2,2,2),
#    #                                               shape=c(NA,NA,NA,NA,NA),
#    #                                               alpha=1)))+
#       theme(legend.position = "bottom")+
#       theme_classic())

# list1111 <- sessionInfo()
# list1111$loadedOnly

# install.packages("ggpmisc")
# library(ggpmisc)

# summary(lm(plan_wide_19902020$CODIGO~plan_wide_19902020$DBO))
# 
# 
# p <- ggplot(data, aes(y=number, x=pod)) +
#   geom_boxplot()
# print(p)

# install.packages("GGally")


# fit = lm(plan_wide_19902020$`Oxigênio dissolvido`~ plan_wide_19902020$CODIGO)
# summary(fit)
# summary.lm(fit)
# 
# plan_wide_19902020$IQA
# 
# plan_wide_19902020 <- plan_wide_19902020 %>% 
#    mutate(IQA = ifelse(IQA == 0, NA, IQA))
# pacman::p_load(esquisse)
```

### Correlação
```{r Correlação, time_it = TRUE}
parametros_IQA <- plan_wide_19902020 %>%
  select(CODIGO,
         pH,
         DBO,
         `Nitrogênio amoniacal`,
         `Nitrogênio total`,
         `Fósforo total`,
         `Temperatura água`,
         Turbidez,
         `Sólidos totais`,
         `Oxigênio dissolvido`,
         Condutividade)

write.csv(parametros_IQA,
          "./parametros_IQA.csv",
          row.names = FALSE)

parametros_IQA %>% 
  select(-CODIGO) %>% 
  ggcorr(method = "complete.obs",
           # "pearson",
           # "pairwise",
         name = "Correlação",
         label = TRUE,
         label_alpha = TRUE,
         digits = 3,
         low = "#3B9AB2",
         mid = "#EEEEEE",
         high = "#F21A00",
         # palette = "RdYlBu",
         layout.exp = 0,
         legend.position = "left",
         label_round = 3,
         )

# Gráfico das correlações entre todos os parâmetros com significância
# correl_IQA <- parametros_IQA %>% 
#   select(-CODIGO) %>% 
#   ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
#           axisLabels = "show")
```

### Condutividade elétrica

```{r Gráfico cond_elet periodo1, warning = FALSE, message = FALSE}
(cond_elet_p1 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"1990" &
                                   ANO_COLETA<="2000"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)
```

```{r Gráfico cond_elet periodo2, warning = FALSE, message = FALSE}
(cond_elet_p2 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2000" &
                                   ANO_COLETA<="2010"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)
```

```{r Gráfico cond_elet periodo3, warning = FALSE, message = FALSE}
(cond_elet_p3 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2010" &
                                   ANO_COLETA<="2020"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)
```

```{r Gráfico cond_elet 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)
```

```{r Sumário cond_elet}
(sum_cond_elet_p1 <- plan_wide_19902020 %>%
   select(CODIGO, Condutividade, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(Condutividade, 
           na.rm = TRUE),
     q1 = 
       quantile(Condutividade, 0.25, 
                na.rm = TRUE),
     median = 
       median(Condutividade, 
              na.rm = TRUE),
     mean = 
       mean(Condutividade, 
            na.rm= TRUE),
     q3 = 
       quantile(Condutividade, 0.75, 
                na.rm = TRUE),
     max = 
       max(Condutividade, 
           na.rm = TRUE))
)

(sum_cond_elet_p2 <- plan_wide_19902020 %>%
    select(CODIGO, Condutividade, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(Condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Condutividade, 
               na.rm = TRUE),
      mean = 
        mean(Condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(Condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Condutividade, 
            na.rm = TRUE))
)

(sum_cond_elet_p3 <- plan_wide_19902020 %>%
    select(CODIGO, Condutividade, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(Condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Condutividade, 
               na.rm = TRUE),
      mean = 
        mean(Condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(Condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Condutividade, 
            na.rm = TRUE),
      n = 
        length(Condutividade))
)

# plan_wide_19902020 %>% 
#    select(CODIGO, IQA) %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#       min = 
#          min(IQA, 
#              na.rm = TRUE),
#       q1 = 
#          quantile(IQA, 0.25, 
#                   na.rm = TRUE),
#       median = 
#          median(IQA, 
#                 na.rm = TRUE),
#       mean = 
#          mean(IQA, 
#               na.rm= TRUE),
#       q3 = 
#          quantile(IQA, 0.75, 
#                   na.rm = TRUE),
#       max = 
#          max(IQA, 
#              na.rm = TRUE))
```

```{r Salvando cond_elet}
ggsave("cond_elet_p1.png",
       plot = cond_elet_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p2.png",
       plot = cond_elet_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p3.png",
       plot = cond_elet_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

```

